Finally, one can restrict the type of movement that is considered, shrinking the number of alignments that are attempted.

of this, no combination of inversions can bring "quickly" between "here" and "ran".


Currently they are set according to the developers' intuition and after the analysis of the results from several trials.

out of 37 competing systems, CoWAL was rated the first, MEBA the 20 th and TREQ-AL (Tufi et al.


For languages with weak inflectional character (as English is) the POS trailing contributes especially to the filtering the search space.

the SVM-based combiner deleted this link, producing the result shown in Figure 3.


They illustrated this on a corpus of Encyclopedia Britannica entries describing world cities, where each entry comes in two flavors, the comprehensive encyclopedia entry, and a shorter and simpler elementary version.

it changes the density of points at different score levels.


given this fact, we impose the three assumptions on followers as shown in (3) so that followers' actions will not overlap the utterances of commanders.


In this paper, we discuss misunderstanding problem in the dialogue involving participant's actions as well as utterances.

(b) The follower immediately stops the action when the commander interrupts him.


Utterances are generally regarded as points on the time-line in dialogue processing.

symbols beginning with a lower case letter are function symbols.


The past research dealing with misunderstanding has been focused on the dialogue involving only utterances.

things in the same DU should be more finely grounded at various levels individually.


(c) The follower will not make action as primary elements while speaking.


Some speakers believed structural marking also to be useful in the HCI situation, for example.

they concern the preconditions for formulating utterances particularly for the respective hearer.


We tackle this problem by testing three different variants of the semi-supervised method for orientation detection proposed in (Esuli and Sebastiani, 2005).

we contend that the plain determination of the orientation of terms is not a realistic problem, since it starts from the nonrealistic assumption that we already know whether a term is subjective or not; this would imply that a linguistic resource that marks terms as "subjective" or "objective" is available, which is usually not the case.


Within opinion mining, several subtasks can be identified, all of them having to do with tagging a given document according to expressed opinion: 1. determining document subjectivity, as in deciding whether a given text has a factual nature (i.e.

while for the former we use well-known learners for binary classification (the naive Bayesian learner using the multinomial model (McCallum and Nigam, 1998), support vector machines using linear kernels (Joachims, 1998), the Rocchio learner, and its PrTFIDF probabilistic version (Joachims, 1997)), for Approach III we use their multiclass versions 9 .


We believe that we can use the obtained basic orientations of phrases for affect analysis of higher linguistic units such as sentences and documents.

the number of distinct nouns is 4770 and the number of distinct adjectives is 384.


Next, we qualitatively evaluate the proposed methods.

for other datasets with more classes, Gaussians might be a good model for P (c|az).


For the words with Net Overlap of ±7 and greater, both STEP and HM had accuracy around 90%.

the Net Overlap scores obtained for each identified word were then used to stratify these words into groups that reflect positive or negative distance of these words from the zero score.


Such disagreements were addressed by computing the Net Overlap Scores for each of the found words: the total number of runs assigning the word a negative sentiment was subtracted from the total of the runs that consider it positive.

the comparisons are performed separately for each of the subsets of GI-H4 that are characterized by a different distance from the core of the lexical category of sentiment.


The analysis of STEP system performance vs. GI-H4 and of the disagreements between manually annotated HM and GI-H4 showed that the greatest challenge with sentiment tagging of words lies at the boundary between sentimentmarked (positive or negative) and sentimentneutral words.

as annotators proceed from the core of the category to its periphery, word mem-bership in this category becomes more ambiguous, and hence, lower inter-annotator agreement can be expected for more peripheral words.


We proposed an automated, quantitative, nearly knowledge-free way to evaluate how biased a corpus constructed in this way is.

our goal is to create a "balanced" corpus of Web pages in a given language; e.g., the portion composed of all Spanish Web pages.


To construct a "balanced" corpus via a search engine one reasonable strategy is to use appropriately balanced query terms, e.g., using random terms extracted from an available balanced corpus (Sharoff, 2006).

we will not motivate our method in terms of whether it favors unbiased samples from the Web, but in terms of whether the documents that are sampled appear to be balanced with respect to a set of deliberately biased samples.


The bootstrap mechanism grows the target term set tenfold, making it very laborious to identify all the valid translation pairs manually.

table 2 gives a summary of the key results.


On a different level, we could also apply the bootstrapping to expand the French set of related terms.

only 3.9 of those pairs are included in M'*.


This is achieved by requiring complete and disjoint coverage of the blocks only for the reference sentence, and not for the candidate translation.

this means that the path from (0, 0) to (i , l) is less expensive than the path from (0, 0) to (i , l).


Due to the small number of samples for evaluation on system level (10 and 5, respectively), all correlation coefficients between automatic and human evaluation on system level are very close to 1.

as the BLEU score is unsuitable for sentence level evaluation in its original definition, BLEU-S smoothing as described by (Lin and Och, 2004) is performed.


Each of these steps can be done in time O(I).

the different evaluation measures were assessed experimentally on data from the Chinese-English and the Arabic-English task of the NISt 2004 evaluation workshop (Przybocki, 2004).


An interesting topic in MT evaluation research is the question whether a linear combination of two MT evaluation measures can improve the correlation between automatic and human evaluation.

an obvious way of comparing the spelling is the Levenshtein distance.


3 Proof: Assume that the long jump edge goes from (i , l) to (i, l), and that there exists an i such that D(i , l) < D(i , l).

the more blocks there are to be reordered between reference and candidate sentence, the higher we want the measure to evaluate the distance between these sentences.


For example, since the hypothesis translation from Table 1 has a length of 18 with 15 unigram matches, 10 bigram matches, 5 trigram matches, and three 4-gram matches, we can artificially construct an identically scored hypothesis by drawing an identical number of matching ngrams from the reference translations.

the manual evaluation conducted for the NISt Mt Eval is done by English speakers without reference to the original Arabic or Chinese documents.


Last year's evaluation exercise (Lee and Przybocki, 2005) was startling in that Bleu's rankings of the Arabic-English translation systems failed to fully correspond to the manual evaluation.

we then performed a manual evaluation where we had three judges assign fluency and adequacy ratings for the English translations of 300 French sentences for each of the three systems.


Applying TBL to font styles resulted in a higher accuracy than applying TBL to tagging.

in the transformation templates, we use the tokens themselves as features, i.e.


We have more appropriately narrowed down the list of proper tags for this position by using complex ambiguity tags, but because of too much incorrect annotation, we still generate the wrong tag.

perhaps more revealing is the improvement in the precision of the changed tokens, from 58.56% to 73.86%.


we performed experiments aimed at analysing how the LM and CM segmentation models behave in predicting segment boundaries at the two different levels of granularity.


Combining methods also seems an interesting track to follow.

the texts were automatically cut into sentences.


We annotated pairs of sentences where textual parallelism led us to rhetorically coordinate them.

the normalized degree of parallelism is D(S 1 , S 2 ) = 2 5+6 × 2.67, which is about 0.48.


For states 1 through 4, the trend is that if the concept has been repeated, the tutor should give feedback or a combination of feedback with another Tutor Act.

if adding a new feature actually does not really change what the tutor should do (that is, the tutor will do the baseline policy regardless of the new information), one can conclude that the feature is not worth including in a student state.


For example, a tutor can give feedback and then ask a question in the same turn.

these 5 frustration states are grouped together in the "threshold state" and our MDP found that the best policy when in that state is to give no feedback.


As long as we have a reward function, Reinforcement Learning allows one to automatically compute the best policy.

we wish to find the best combination of states and actions to maximize the reward at the end of the dialogue.


The second example displays a case in which our system and the human system are grammatical, but the removal of a prepositional phrase hurts the resulting meaning of the sentence.

essentially , design recovery tools read existing code and translate it into the language in which CASe is conversant -defi nitions and structured diagrams .


Of course, like most compression and translation tasks, this is not true, consider, TapeWare , which supports DOS and NetWare 286 , is a value-added process that lets you directly connect the QA150-EXAT to a file server and issue a command from any workstation to back up the server The human annotated compression is, TapeWare supports DOS and NetWare 286.

in this paper we have described a new system for sentence compression.


If we assume that C[j] is the highest scoring compression that ends at word x j , for all j < i, then C[i] must also be the highest scoring compression ending at word x i since it represents the max combination over all high scoring shorter compressions plus the score of extending the compression to the current word.

the judges were told all three compressions were automatically generated and the order in which they were presented was randomly chosen for each sentence.


A user may specifically want a 25% compression rate for all sentences.

our model never failed in such drastic ways and always output something reasonable.


MEAD: I am a software engineer and am very keen into technical details of everything i buy , i spend around 3 months before buying the digital camera ; and i must say , g3 worth every single cent i spent on it .

summarizing a corpus of evaluative text is much more similar to summarizing conflicting reports than a consistent set of factual documents.


The SUMTIME system builders believe deviating from corpus frequency in such cases makes SUMTIME texts better from the reader's perspective, and it does appear to increase human ratings of the system; but deviating from the corpus in such a way decreases the system's score under corpus-similarity metrics.

the SUMTIME corpus contains both the inputs (numerical weather predictions) and the outputs (forecast texts) of the forecast-generation process.


Disagreement on spatial domains was mostly due to the problem of reciprocal pairs, where participants disagreed on whether entities such as e 8 and e 9 in Figure 1 gave rise to a well-formed cluster or not.

a similar case arises with plural disjunctive descriptions of the form λx[p(x)∨q(x)], which are usually realised as coordinate constructions of the form the N' 1 and the N' 2 .


Using majority-rules voting with Learners A and D doubled, we were able to obtain an average f-score of 48.4%, showing that voting does to an extent balance out the learners' varying results on different words.

literal precision is defined as (correct literals in literal cluster / size of literal cluster).


Moving synsets or feature sets can add valuable content to one feedback set while removing noise from the other.

even before voting, we attempt to improve the correctness of initial attractions through the use of SuperTags, which allows us to add internal structure information to the bag-of-words feature lists.


We investigate the lexical and syntactic flexibility of a class of idiomatic expressions.

here we demonstrate that combining them into a single measure of fixedness, while giving more weight to the better measure, results in a more effective predictor of idiomaticity.


The displayed versions were counterbalanced so that every subject performed each pairwise comparison of models twice, once in each order.

(2004) found that, among schoolchildren, girls preferred a tutoring system that included an animated agent, while boys preferred one that did not;  found that a more expressive talking head decreased male subjects' task performance when using the full COMIC system; while Bickmore and Cassell (2005) found that women trusted the REA agent more in embodied mode, while men trusted her more over the telephone.


In this approach, a declarative multimodal grammar captures both the structure and the interpretation of multimodal and unimodal commands.

we use the Adaboost classifier (Freund and Schapire, 1996) wherein a highly accurate classifier is build by combining many "weak" or "simple" base classifiers j , each of which may only be moderately accurate.


Another disadvantage is that the translation can add more ambiguity to the parsing problem.

an S clause headed by a complementizer is converted to an SBAR.


The homomorphisms are linear and complete because the tree relation between a tree-substitution grammar derivation tree and its associated derived tree is exactly a linear complete tree homomorphism.

because they compute only the identity function, tree automata are of interest for their domains, not the mappings they compute.


We will thus take the nodes of TAG trees to be labeled with symbols from a ranked alphabet F; a given symbol then has a fixed arity and a fixed number of children.

these additional symbols, since they are nullary, will necessarily appear only at the frontier of trees.


The most common case consists in merging the tokens' attributes to form a more specific expression.

we  Table 2: Evaluation results for final ordering averaged per text (with P , R, and F in %).


Evaluation is performed against manual and semiautomatic methods using a fixed set of answer labels.

another disadvantage of predefined answer types is with regard to granularity.


In this work, we propose a supervised machine learning approach to relation extraction which is applicable even when (deep) linguistic processing is not available or reliable.

we use a combination of kernel functions to integrate two different information sources: (i) the whole sentence where the relation appears, and (ii) the local contexts around the interacting entities.


Third, 175 different normalized templates were identified.

the annotation and analysis methodologies are general and are suggested as highly effective tools for further research.


Label sharing between EPs indicates conjunction (e.g., in Fig 1, big, angry and dog share the label l2).

it retains information about a quantifier's N , since this forms the restrictor of the generalised quantifier (for instance Most white cats are deaf has different truth conditions from Most deaf cats are white).


There is usually a clear sense distinction when this occurs.

models for an MRS may be defined in a language with or without argument labels.


NLP tagging is then an add on to a semantic layer that in itself can already be very rich and of high quality.

i would like to thank Willemijn Heeren, Roeland Ordelman and Stef Scagliola for their role in the genesis of ideas and insights.


Candidate sentences are determined based on word overlap and the decision whether a sentence pair is parallel or not is performed by a maximum entropy classifier trained on parallel sentences.

two correct translations may differ in the order in which the words appear, something that WER is incapable of taking into account as it works on word to word basis.


As in earlier approaches, we also use a lexicon to set priors, but we explore new uses of a Dictionary of Affect in Language (DAL) (Whissel, 1989) extended using WordNet (Fellbaum, 1998).

value of the norm is lower for goodies than for good.


As the words are linked to the concepts by directed edges, they act as source nodes injecting mass into the concepts they are associated with, which thus become relevant nodes, and spread their mass over the LKB graph.

then, the calculation of the PageRank vector Pr over G is equivalent to resolving Equation (1).


Note that it was not our aim to improve the results of the basic classifiers on this scenario, but given the fact that we are going to apply all these techniques in the domain adaptation scenario, we need to show these results as baselines.

that the domain adaptation kernel combination approach, no matter the amount of target data, is always above the rest of the classifiers, showing the robustness of our approach.


In order to illustrate why the CBC-NER systems are beneficial, we give below some examples taken from the test corpus for which the CBC system A had allowed to improve the performances by respectively disambiguating or correcting a wrong annotation or detecting corpus-specific NEs.

then, we need to construct a NE resource where for each pair (NE x syntactic context) we have an annotation.


So far, the different steps that we have introduced were unsupervised.

we precise that for an occurrence of a NE e i its associated local context is the set of syntactical dependencies c j in which e i is involved.


Our proposal is close to (Cucchiarelli and Velardi, 2001) as it uses syntactic relations ( §2.2) and as it relies on existing NER systems ( §2.6.2).

then, we construct the matrix t of general term: where cli k is an element of CLI.


in this work, the authors found a significant correlation between the similarity of the time series distribution of a word and the likelihood of being a NE.


It also allows to identify new types of annotation.


The average Levenshtein edit distance (with words as the atomic unit, and with the score normalized to the interval [0,1]) between the original MT out- puts and the corrected sentences made by participants using The Chinese Room is 0.59; in contrast, the edit distance is shorter, at 0.40, when participants correct MT outputs directly.

in the next few subsections, we examine how these factors correlate with the quality of the participant corrections.


Sarikaya and Deng use Joint Morphological-Lexical Language Models to rerank the output of an English-to-Arabic MT system.

we note that the gain achieved from the reordering of the nonsegmented and segmented systems are comparable.


This rule is motivated by the order of the adjective with respect to its head noun, as well as the idafa construct (see Examples 6 and 7 in Section 2.2.


If the score improves, then the sentence in question is replaced with the baseline system's translation, otherwise it remains unchanged and we move on to the next one.

of applying this rule, the phrase the blank computer screen becomes the screen computer blank .


These constraints contribute to robustness, but cannot be used with the connection path method, since any connection path (parsing action) suggested by the classier can be incorporated into the incremental parse tree.

the label for each input feature vector is the parsing action (for subtask classication and prediction) or the dialog act label (for dialog act clas-sication and prediction).


A root word-form lexicon is in some cases more useful than a stem lexicon, for the following reasons: 1.

certain stemming rules will therefore need to be duplicated in order to accommodate the different spelling possibilities and the different vowel representations in Hindi.


Obviously, the Italian reference lexicon does not contain such information.

the acquisition of the contrastive knowledge in a Mt context is indeed the most essential issue in this kind of approach.


Amongst those, there are a number of erroneous words that are homographic to a constructed neologism.

we looked for a simple way to automatically extend the Italian lexicon.


Similarly, the metrics proposed for text generation by  (simple accuracy, generation accuracy) are based on string-edit distance from an ideal output.

correlation analysis reveals that the structural features are significant but weakly correlated with fluency.


The long noun phrase is more difficult to read, especially in subject position.

the work of (Wan et al., 2005) and (Mutton et al., 2007) directly sets as a goal the assessment of sentence-level fluency, regardless of content.


We present a new system for resolving (personal) pronoun anaphora 1 .

for example, gender distributions start with the probability of each gender equal to one-third.


Some of these systems provide their own preprocessing tools.

it follows from this that nonanaphoric pronouns come in two basic varietiessome have a referent, but because the referent is not mentioned in the text 2 there is no anaphoric relation to other text.


Our work improves upon theirs in several dimensions.

the system does not learn to recognize non-anaphoric pronounsa significant problem.


We have tested an approximate solution that allows for fast computing.

although the observed word error rate reductions are mostly smaller than the corresponding perplexity reductions, the results are actually very good, when we consider the fact that considerable reductions in perplexity may typically translate into meager word error reductions; see, for instance, Rosenfeld (2000), Goodman (2001).


When scoring the remaining n-grams, linear scaling is avoided, because the majority of the ngrams are irrelevant or neutral with respect to our domain of interest, and many of them would obtain fairly high relevance values.

kneser-Ney outperformed Good-Turing, but the improvements were small, and a statistically significant difference was measured only for the 40 MB LMs.


There are however some surprising results.

this computation require a sum over all the possible alignments, whose numbers grow exponentially with the size of the sentences.


For the 10 categories of Table 1 used in previous work, we  have obtained (P=92,T=41) which outperforms the seed-based concept acquisition of (Widdows and Dorow, 2002;Davidov and Rappoport, 2006) (P=90,T=35) on the same concepts.

the two main algorithmic approaches are pattern-based discovery, and clustering of context feature vectors.


String features representing each proposition in the conversational record in s k (with filtered gensym identifiers).

as the examples of Section 2.3 illustrate, COREF's interactions with its users offer substantially more information about interpretation than the raw text generally used for co-training.


This is an interesting result since it shows that (a) richer structures do produce better ranking models and (b) kernel methods give a remarkable help in feature design.

the SCLM is trained using SRILM tools and then converted to an FST.


As pointed out by (Szpektor et al., 2007) a thorough evaluation of a rule collection is not a trivial task; however due to our methodology we can assume that the percentage of rules eliminated this way that are indeed contradictions gets close to 100%.

given the preliminary stage of this system, the overall results show very low improvement over a random classification baseline.


The more complex rules are even more difficult to capture with a DIRT-like algorithm.

the focus of this paper is to evaluate the application of inference rules on RTE, instead of exploring methods of tackling the task itself.


To measure the working memory burden of a text, we'd like to capture the number of discourse entities that a reader must keep in mind.

we conducted two rounds of training and evaluation of our three regression models.


The texts come from online and commercial sources, and some have been analyzed previously by text simplification researchers (Petersen and Ostendorf, 2009).

after creating this tool, we can conduct further reading comprehension experiments with adults with ID to determine what threshold (for readability scores assigned by our tool) is appropriate for our users.


It is important to note that, compared with the baseline, we did not do any parameter tuning for λ and the inter-sentence similarity threshold.

there are also important differences between the two tasks.


One way to improve the segmentation and tagging accuracy is to allow non-deterministic segmentation and tagging for Chinese for the reasons stated in Section 1.

segmentation and tagging are part of the parsing process.


Word segmentation and tagging are the necessary initial steps for almost any language processing system, and Chinese parsers are no exception.

while a deterministic approach to Chinese segmentation and POS tagging might be appropriate and necessary for certain tasks or applications, it has been shown to suffer from a problem of low accuracy.


Following a strategy widely used in Chinese word segmentation, we did this by regarding the problem as a character tagging problem.

even though PKU's tokenizertagger is one of the state-of-art systems, its performance is not satisfactory for the Chinese LFG.


None of these treebanks contain structures that are strongly ill-nested for their gap degree.

the gap degree of the dependency tree t is the maximum among the gap degrees of its nodes.


Such encyploedic knowledge can advance vertical search engines such as http://www.spock.com that are focused on people searches where one can get an enhanced search interface for searching by various biographic attributes.

even if the occupation is not explicitly mentioned in the article, one can infer it using a bag-of-words topic profile learned from the seed examples.


The morphotactics of the Tigrinya verb is replete with dependencies which span the verb stem: (1) the negative circumfix ay-n, (2) absence of the negative suffix -n following a subordinating prefix, (3) constraints on combinations of subject agreement prefixes and suffixes in the imperfective and jussive, (4) constraints on combinations of subject agreement affixes and object suffixes.

this presents problems in the case of non-concatenative morphology: discontinuous morphemes (circumfix-ation); infixation, which breaks up a morpheme by inserting another within it; reduplication, by which part or all of some morpheme is copied; and the template morphology (also called stempattern morphology, intercalation, and interdigitation) that characterizes Semitic languages, and which is the focus of much of this paper.


Tigrinya morphology is complex in two senses.

unlike the former two types, the TAM category of the verb is redundantly coded for by the combination of subject agreement affixes.


9 In what follows, a lexical event is a word segment which is assigned a single POS thereby functioning as a leaf in a syntactic parse tree.

(2008a) report that roughly 4.5% of the tokens in a 42M tokens corpus of news text are unknown to the Lexicon.


This is a strong assumption, as the segmentation stage is ambiguous, and segmentation information provides very useful morphological hints that greatly constrain the search space of the parser.

the rest (less than a thousand instances) were manually assigned.


Hebrew has a wide coverage lexicon / morphological-analyzer (henceforth, KC Analyzer) available 2 , but its tagset is different than the one used by the Hebrew Treebank.

unlike the oracle segmentation case, here the semisupervised lexical probabilities (LexProbs) have a major effect on the parser performance (∼ 69 to ∼ 73.5 F-score), an overall improvement of ∼ 6.6 F-points over the Baseline, which is the previous state-of-the art for this joint task.


Similarly, if "school" is withheld from the topic word features when the privacy rights topic is the test set, the accuracy rises to 100%.

mosteller and Wallace (1964) used function words in a groundbreaking study that identified authors of The Federalist Papers.


This coefficient shows a global agreement between all the judges, which goes beyond Cohen's Kappa coefficient.

following the summary in (Yagi, 2000), the ear-voice-span is variable in duration depending on some source and target language variables, like speech delivery rate, information density, redundancy, word order, syntactic characteristics, etc.


To make the description of these features more concrete, we show the feature templates in Table 2.

nevertheless, we believe the Bengali Wikipedia will become a useful resource for language processing as its size increases.


First, the number of induced affixes is large, and using only a subset of them as features could make the training process more efficient.

their bootstrapping approach is tested on Romanian, English, Greek, turkish, and Hindi.


Their bootstrapping approach is tested on Romanian, English, Greek, Turkish, and Hindi.

for each of the three NE classes shown on the left of Table  1, we compute a weighted sum of its keywords: a keyword that appears in the first paragraph has a weight of 3, a keyword that appears elsewhere in the article has a weight of 1, and a keyword that does not appear in the article has a weight of 0.


In view of the above problems, many learningbased Bengali NE recognizers have relied heavily on manually-constructed name lists for identifying persons, organizations, and locations.

in addition to POS information, each sentence in the corpus is annotated with NE information.


Otherwise, we simply sample from the distribution in Figure 2  As mentioned in the introduction, the (wordbased) POS lexicons used in existing approaches to unsupervised POS tagging were created somewhat unrealistically by collecting the possible POS tags of a word directly from the corpus on which the tagger is to be evaluated.

we automatically determine the values of these hyperparameters by (1) randomly initializing them and (2) resampling their values by using a Metropolis-Hastings update (Gilks et al., 1996) at the end of each sampling iteration.


As we can see from the partial POS lexicon for English in Figure 1, "the" is unambiguous with respect to POS tagging, since it can only be a determiner (DT), whereas "sting" is ambiguous, since it can be a common noun (NN), a proper noun (NNP) or a verb (VB).

schütze (1995) and Clark (2000) apply syntactic clustering and dimensionality reduction in a knowledge-free setting to obtain meaningful clusters.


This motivates a fully-Bayesian approach, which, rather than committing to a particular set of parameter values as in an EM-based approach, integrates over all possible values of θ and, most importantly, allows the use of priors to favor the learning of the skewed distributions, through the use of the term P (θ|w) in the following equation: The question, then, is: which priors on θ would allow the acquisition of skewed distributions?

our approach imposes suffix-based constraints on the tagging of words that are unseen w.r.t.


More recently, a fully-Bayesian approach to unsupervised POS tagging has been developed by Goldwater and Griffiths (2007) [henceforth G&G] as a viable alternative to the traditional maximumlikelihood-based HMM approach.

1 the labor-intensive POS lexicon construction process renders unsupervised POS taggers a lot less unsupervised than they appear.


The phrase model retains the word alignment information found during training.

in the joint factored model, we have both surface forms and POS tags available to train the lexicalized reordering models on.


This is possible only when the segmentation is identical for both source and target span of each phrase pair in each translation model.

xia and McCord (2004) automatically create rewrite rules which reorder the source sentence.


all rules are kept) with slight translation improvements.

phrase pairs as used in phrasebased translation), rules can be classed by their number of non-terminals, N nt , and their number of elements, N e .


This filtering leads to a degradation in translation performance (see Table 2 of Shen et al.

reducing the cutoff for classes 1.3 and 2.4 to 3 and 5, respectively, adds many more rules with no increase in performance.


Possibly, their combination could improve the overall results since they offer different semantic perspectives of the data.

as expected, the results of the systems increase while augmenting the level of abstraction (from senses to SuperSenses), and almost in every case, the baseline results are reached or outperformed.


Combining them, we generated the set of experiments described later.

most of the knowledge required for the class-based WSD system seems to be already present on a small part of SemCor.


In fact, the algorithm only considers the relative number of relations of each synset along the hypernymy chain.

depending on the type of relations considered to be counted and the threshold established, different sets of BLC can be easily obtained for each WN version.


The reranking experiments were conducted using the reranker by Collins and Koo (2005).

the experiments in which the parser was forced to assume predefined scopes show that the scope information is important for parsing quality.


An evaluation of only sentences with coordination shows that there is a noticeable difference: the F-score reaches 67.28 (precision: 66.36%, recall: 68.23%) as compared to 69.73 for the full test set.

the F-score is also minimally higher than the F-score for experiment 2 (gold scope), and recall is increased by approximately 1 percent point (even though only 12.5% of the sentences were reranked).


If the same parser is used for this step and for the final parse, we can be certain that only scopes are extracted that are compatible with the grammar of the final parser.

complex coordinations can take up a considerable part of the input string and accordingly of the overall sentence structure.


Since German has a relatively free word order, it is impossible to deduce the grammatical function of a noun phrase from the configuration of the sentence.

after pruning all features that occurred in the training data with a frequency lower than 5, the extractions produced more than 5 mio.


The overall score of a sentence is computed as the weighted linear combination of the sentence and word scores.

evidence of the usefulness of key fact extraction is CNN's web site which since 2006 has most of its news articles preceded by a list of story highlights, see Figure 1.


Our approach handles the transliteration task as a case of phrase-based SMT, based on the noisy channel model.

minimum error rate training was done on the development set to optimize translation performance obtained by the training phase.


Even letters that are considered as representing vowels may sometimes represent consonants, specifically ו [v]/[o]/ [u] and י [y]/ [i].

the penultimate column shows the results obtained by our method combining the SVM with surface-based features.


Our initial approach consisted of consulting only the decision of the one-class SVM.

distinctive features are the characteristics that define the set of phonemic segments (consonants, vowels) in a given language.


Instead, we extracted a parallel list of Hebrew and English terms from Wikipedia and automatically generated such a corpus.

the mapping between Hebrew orthography and phonology is highly ambiguous.


Should we pair every markable (even from the beginning of the text) with every other succeeding markable?


Notice that one RMRS may be satisfied by multiple trees; we can take the RMRS to be a partial description of each of these trees.

1, x 1 to x, and the root label every q 1 is consistent with the EP predicate every q.


As an example, Steedman and Baldridge's (2009) analysis of Shieber's (1985) Swiss German subordinate clause (das) mer em Hans es huus hälfed aastriiche ('(that) we help Hans paint the house') is shown in Figure 1.

now that we know how to see PF-CCG derivations as dependency trees, we can ask what sets of such trees can be generated by PF-CCG grammars.


An important benefit of this approach is that it allows one to make use of the available grammars and parsers for language modeling task.

second, syntactic analysis is performed on the sentence level.


We propose to attack this problem using chart-parsing algorithms developed for mildly contextsensitive grammar formalisms.

for a grammar G extracted by our technique, the number f equals the maximal block-degree per node.


The Mutual Expectation measure as described by Dias and Kaalep (2003) is used to measure the degree of cohesiveness between words in a text.

this way we can extract larger Dutch chunks that correspond to several French chunks, for instance: Fr: feu régulateur -de pression carburant.


In order to have insights in the performance of our terminology extraction module, without considering the validity of the bilingual terminology pairs, we contrasted our extracted English terms with state-of-the art monolingual terminology systems.

one can argue that, just because of the syntactic resemblance of both languages, the need for complex multiword terms is less prominent in closely related languages as translators can just paste smaller noun phrases together in the same order in both languages.


On the one hand, accept-ing misrecognized hypotheses leads to misunderstandings and unintended system behaviors which are usually difficult to recover from.

in our experiments, we also use recognizer confidence scores and a limited number of acoustic-prosodic features (e.g.


The method achieved a significant relative reduction of Word Error Rate (WER) of 5% (this is 44% of the possible WER improvement on this data), and 62% of the possible semantic improvement (Dialogue Move Accuracy), compared to the baseline policy of selecting the topmost ASR hypothesis.

if a hypothesis is erroneous as far as its transcript is concerned   The baseline for our experiments is the behavior of the Town-Info spoken dialogue system that was used to collect the experimental data.


Though the particulars of this model are unique, fundamentally it is closest to the work of Hu and Liu (2004a) and Carenini et al.

this optimization is well studied and can be solved with a wide variety of techniques.


business news like the WSJ, or very large balanced corpora, because in both types of corpora one can expect the length of the variations to be quite large.

we ran 10-fold cross-validation, using the exact same data-splits as used in (Loftsson, 2006), both before error correction (i.e.


Here the subject is "enginn" and the object is "það".

intuitively, the variation n-gram method is most suitable for corpora containing specific genres, e.g.


Our work is strongly influenced by Goodman (1999) and Eisner et al.

the logics conveniently specify the full search space, which forms a hypergraph (Klein and Manning, 2001).


characters for Chinese or kana for Japanese).

such segmenters cannot produce consistently good results when used across different domains.


for example, the rule mammal ⇒ whale may not be valid in most cases, but is applicable in universally quantified texts like "mammals are warm-blooded".


We filled this gap by running comparative evaluations of several knowledge-based and corpus-based measures on a data set of short student answers.

in this paper, we explore and evaluate a set of unsupervised techniques for automatic short answer grading.


", the sentence "At least 99% of all animal species are invertebrates, comprising .

information as the best model is PSK+STK+SRK, which improves BOW from 24.2 to 39.1, i.e.


Our experiments with Support Vector Machines on question/answer classification show that our kernels can be used to greatly improve system accuracy.

u is a subsequence of s if there is a sequence of indexes is the distance between the first and last character of the subsequence u in The set of all substrings of a text corpus forms a feature space denoted by F = {u 1 , u 2 , ..} ⊂ Σ * .


Although the ability to detect these patterns is beneficial for a QA system as it improves its overall accuracy, it is slightly misleading for the study that we are carrying out.

we note that: (a) BOw achieves very high accuracy, comparable to the one produced by STK,i.e.


According to Ladefoged (2005) "two sounds in different languages should be considered as distinct if we can point to a third language in which the same two sounds distinguish words".

the repertoire of sounds that make up the sound inventory of a language are not chosen arbitrarily even though the speakers are capable of producing and perceiving a plethora of them.


More recently, Mukherjee and his colleagues (Choudhury et al., 2006;Mukherjee et al., 2007;Mukherjee et al., 2008) studied this problem in the framework of complex networks.

note that spectral analysis, however, has been successfully employed in the domains of biological and social networks (Farkas et al., 2001;Gkantsidis et al., 2003;Banerjee and Jost, 2007).


The consonants are once again found to get clustered into three groups, though not as clearly as in the previous case.

the power-law exponents at the positive and the negative ends are -1.33 (the R 2 value of the fit is 0.98) and -0.88 (R 2 ∼ 0.92) respectively.


We also included in the table the performance of the best-ranking system in the Senseval- Prec  Table 2: Gloss WSD performance on WordNet.

overall, the figures are higher than in previous experiments, thanks to a lower average degree of polysemy of the resource, which also impacts positively on the FS baseline.


Our call to the CQC algorithm in Table 1 is then CQC-Algorithm(flesh 3 n , fruit n ).

first, a dictionary graph is built from the input lexical knowledge resource.


Word Sense Disambiguation is a large research field (see (Navigli, 2009) for an up-to-date overview).

circular edge sequences (possibly with one edge reversed) relating a source to a target word sense.


Further, we say that a path is (quasi-)cyclic if it forms a (quasi-)cycle in the graph.

given a random baseline of 51.69%, the performance of CQC, over 89% precision and recall, is significantly higher.


The experiments revealed that deterministic parsing with default unification achieved high accuracy, with a labeled F-score (LF) of 87.6% for Section 23 of the Penn Treebank with gold POSs.

the best-first algorithm with this objective function searches like the breadth-first search, and hence, parsing is very slow or cannot be processed in a reasonable time.


to handle titles more comprehensively, we compiled a list of the terms most frequently linked immediately prior to PER links.


Following Silverstein (1976) several animacy hierarchies have been proposed in typological studies, focusing on the linguistic category of animacy, i.e., the distinctions which are relevant for linguistic phenomena.

if we examine the errors for the inanimate class we indeed find evidence of gradience within this category.


the latter replaces specific attributes and instances from the query with common placeholders, e.g., "X for Y"; 3) construction of a reference internal vector representation for a small set of seed attributes provided as input.


More precisely, for each hierarchy concept for which attributes must be extracted, the instances associated to all class labels linked under the subhierarchy rooted at the concept are collected as a union set of instances, thus exploiting the transitivity of IsA relations.

evaluation results indicate that the accuracy of the extracted lists of attributes is higher by 8% at rank 10, 13% at rank 30 and 18% at rank 50, when using the automatically-extracted classes of instances rather than the comparatively more numerous and a-priori more reliable, humangenerated, collaboratively-vetted classes of instances available within Wikipedia (Remy, 2002).


We therefore investigated the benefits of a committee-based or ensemble approach.

the lower the rank, the better the performance of the system.


will have as one of the question contexts "X hosted Olympics."

for random, the candidate answers are randomly shuffled and performance is averaged over a number of runs (100).


Scoring answer candidates is complicated by the fact that a gold standard for appropriateness scores does not exist.

they make use of multi-class learning with a Sparse Network of Winnows (SNoW) and a two-layer class hierarchy comprising a total of fifty possible answer types.


the realizer used to collect the MATCH project data).

providing another constraint, or the user can quit (userQuit).


This suggests that one should never generate a SUMMARY.

rL is the only policy which significantly improves the next most likely user action by adapting to features in the current context.


After making the change in the head annotation, we just need to decrease the frequencies of the old trees by one unit, and increase the ones of the new trees by one unit.

in figure 5, algorithm 1 (right) gives the pseudo-code for the algorithm, and the tree (left) shows an example of its usage.


Unsupervised head assignment algorithms can be used for the many applications in NLP where information on headedness is needed to convert constituency trees into dependency trees, or to extract head-lexicalised grammars from a constituency treebank.

2 the authors' approach of using EM for inducing latent information in treebanks has led to extremely accurate constituency parsers, that neither make use of nor produce headedness information; see (Petrov et al., 2006) then describe our evaluations of these algorithms.


The model approximates stretches of F 0 by employing a phonetically motivated model function (Möhler, 1998).

distributions can be compared directly -irrespective of the different number of data points.


The central idea here is that frequently occurring behaviours undergo processes of entrenchment, they become in some sense routine.

the textual transcription of the corpus was annotated with respect to information status using the annotation scheme proposed by Riester (2008).


This is given by: where i and j are vectors of the same pitch accent category and • represents the dot product.

there is a continuum of frequency of occurrence, high to low, from L * H to H * .


The last row in Table 3 shows the results of classifying the training data.

maskey and Hirschberg (2005) found that prosodic features (see Ward, 2004) combined with structural features are sufficient to summarize spoken news broadcasts.


We would also like to thank Timo Baumann and Michaela Atterer for their contributions to the project, as well as Anna Iwanow and Angelika Adam for collecting and transcribing the data used in this paper.

the smallest unit of information that is communicated along the connections is called the incremental unit (IU), the unit of the "minimal amount of characteristic input".


For example, if the system has already clarified the first part of the number sequence due to an ambiguity, it does not need to repeat this part of the sequence again.

second, to investigate whether incremental processing can help us to better model certain aspects of human behaviour in dialogue systems -especially turntaking and feedback -and whether this improves the user's experience of using such a system.


No evaluation is needed to confirm that an incremental system such as this allows more flexible turn-taking and that it can potentially respond faster -this is so by design.

this model is then consulted by an action manager which decides what the system should do next.


Finally, we implemented a supervised classifier.

for example, Riehemann (2001, p. 34) found that for decomposable idioms 75% of the occurrences are in canonical form, rising to 97% for non-decomposable idioms.


In the following sections, we present the best performing set of feature templates as determined on the development data set using only the supervised training setting; our feature templates have thus not been influenced nor extended by the unsupervised data.

8 We can express it as where w(C, T ) is the transition weight for tag T in context C, n is the number of features, α i is the weight coefficient of the i th feature and φ i (C, T ) is the evaluation of the i th feature for context C and tag T .


for repeating the results and significance testing.

czech and English are morphosyntactically very different languages, therefore each of them needs a different set of feature templates.


The training algorithm is very simple-only integer numbers (counts and their sums for the averaging) are updated for each feature at each sentence with imperfect match(es) found against the gold standard.

the best results (on the development data set) have been achieved with the unsupervised chunks containing approx.


Since MCKP is an NP-hard problem, it cannot generally be solved in polynomial time under a reasonable assumption that NP =P.

the goal is to find a subset S(⊂ D) that covers as many conceptual units as possible.


It first performs the forward Viterbi algorithm to obtain the best sequence, storing the upper bounds that are used for pruning in branch-and-bound.

generative models based on lexicalized PCFgs enjoyed great success as the machine learning framework for full parsing (Collins, 1999;Charniak, 2000), but recently discriminative models attract more attention due to their superior accuracy (Charniak and Johnson, 2005;Huang, 2008) and adaptability to new grammars and languages (Buchholz and Marsi, 2006).


The probabilities of the derivations should then be marginalized over to produce the probability of a parse tree, but in this paper we ignore this effect and simply focus only on the best derivation.

semi-Markov conditional random fields (semi-CRFs) can directly handle the chunking problem by considering all possible combinations of subsequences of arbitrary length (Sarawagi and Cohen, 2004).


MINT reconstructed the pairings using the cross-language document similarity model and subsequently mined NETEs.

we also experimented with an extension of He's w-HMM model (He, 2007).


The difference in accuracy for a given time-out value can be considerable: as much as 12% for time-outs around 30 seconds of CPUtime.

for instance, if we chose the trigram filter as our context size, then the training data can be preprocessed in order to store all goal-trigrampairs with frequency above the threshold τ .


On the one hand, the Alpino Treebank might not be a reliable test set for the Alpino parser, because it has been used quite intensively during the development of various components of the system.

generally, models that take into account larger parts of the history require more data to obtain good accuracy, but they are also faster.


Our model is also compared our model with results from the implementation of Munteanu (2006) using Okapi (F), and the results from a combination of our model with Okapi (G).

the following highlight the number 25, 11, and 50 as linguistic-independent-units for the two sentences.


Alignment is based on similarity of content where the same story or event is mentioned.

its performance may be compromised due to the lack of linguistic knowledge, particularly across corpora which are linguistically very different.


Let and be the term list of and respectively, the similarity score in our model is: Finally, we apply the results of time-series research to replace Pearson's correlation which is used in the baseline model, in our calculation of the similarity score of two frequency distributions.

the distributions for single words may have much less similarity.


We apply LMO in a rightward manner beginning with a start-of-sentence token.

this problem is to find a weighted alignments between objects of two distinct sets, where an object from one set is uniquely aligned to some object in the other set.


An edge is penalised if it is improbable that the head takes on yet another modifier, say in the example of an attachment to a preposition whose argument position has already been filled.

unsurprisingly, the improvements are most evident in the baselines which rely heavily on the language model.


We use negative log probabilities so that edge weights will correspond to costs.

all algorithms and baselines compared would suffer equally in this respect, and so this will be less problematic when averaging across multiple test cases.


In the case presented here, this frequency is 1 (the nearest neighbour).

a good measuring device for any linguistic feature is therefore one which strongly differentiates real language from random data.


Schulte im Walde & Melinger (2008) found that the correlation between co-occurrence derived association scores and human association norms were weakly dependent upon the window size used to calculate the former, but that certain associations tended to be represented at certain window sizes, by virtue of the fact that the best overall correlation was found by combining evidence from all window sizes.

put, the figures show the percentage of times a given association score or lower was measured between word types in a corpus which is known to be devoid of any actual syntagmatic association.


First, it cannot handle the unseen language problem: if an IGT in the test data belongs to a language that does not appear in the training data, this approach cannot classify it correctly.

in order to take advantage of the context information, we trained several classifiers (e.g., decision tree, Naive Bayes, and maximum entropy) using the Mallet package (McCallum, 2002) and a SVM classifier using the libSVM package (Chang and Lin, 2001).


The resulting conditional probabilities adequately represent the proximity of the involved vertices.

to overcome this problem inflation vertices i a and i b of strength 1 with accompanying feature vertices are introduced to 1 http://www.ims.uni-stuttgart.de/projekte/corplex/treetagger/ the graph.


10 Though different techniques have been developed to discount the over-confidence (Kolcz and Chowdhury, 2005), we use the most basic one: we raise the likelihood term in power 1 T , where the parameter T is chosen empirically.

we keep this particular representation, in part to compare with the previous work.


The second (cos (N 1 , N 2 )) applies a threshold (determined on the test set) to the cosine similarity of the pair.

a relation of feature inclusion between the narrower antecedent and broader consequent terms captures both AN |= N and N 1 |= N 2 entailment.


In addition, we plan to lift the restriction that the parse yield is known in advance, in order to allow for evaluation of joint parse-segmentation hypotheses.

2011, we demand set inclusion: T-Subsumption, denoted t , is a relation between multi-function trees that indicates that a tree π 1 is consistent with and more general than tree π 2 .


It frequently uses the same grammatical structures (like "Stock indexes rose X percent at the Y Stock on Wednesday") and the lexicon is also limited.

there is a noticeable difference between the "newspaper" and the "short business news" corpora.


In English, high or low PP-attachment was responsible for many parsing ambiguities: most typically, the prepositional complement which follows the head was attached to the verb instead of the noun or vice versa.

the Szeged Dependency treebank  was constructed by first automatically converting the phrase-structure trees into dependency trees, then each of them was manually investigated and corrected.


Here, we applied the Conditional Random Fields-based Stanford POS tagger (Toutanova et al., 2003) and carried out 5-fold-cross POS training/tagging inside the subcorpora.

in Hungarian, morphological information is of help in such sentences, as it is not the position relative to the verb but the case suffix that determines the grammatical role of the noun.


, n} is the set of nodes, and E ⊆ V w × V w is the set of directed arcs.

its SHiFT, LEFT-ARC, RiGHT-ARC and REDUCE transitions work similarly to those in the planar parser, but while SHiFT pushes the first word in the buffer to both stacks; the other three transitions only work with the top of the active stack, ignoring the inactive one.


While previous work using third order factors, cf.

due to the dynamic programming approach, the features cannot use arbitrarily complex structural configurations: otherwise the dynamic programming chart would have to be split into exponentially many special states.


(Shen and Klakow, 2006) also describe a method that is primarily based on similarity scores between dependency relation pairs.

most of these approaches make use of Word-Net::Similarity, a Perl software package that measures semantic similarity (or relatedness) between a pair of word senses by returning a numeric value that represents the degree to which they are similar or related (Pedersen et al., 2004).


This helps to create an abridged version of the document that can quickly produce the snippet.

in this section, we examine a collection of queries submitted to Yahoo!


The structure of queries is very different from the standard phrase structure: queries are very short and the word order might be different than the typical full phrase one.

ideally, we would have liked to combine the two approaches we proposed: use the querygenre-tuned model to produce the Nbest list which is then reranked to optimize the MAP score.


Indeed, the local reorderings are much more frequent between English and French (e.g.

we may distinguish two main groups of approaches to CLIR: document translation and query translation.


The only exception is posmaplex model (based on PoS tagging) for German which can be explained by the fact that the German grammar used for query processing was not adapted for queries as opposed to English and French grammars.

a vector of sparse features is introduced where each component corresponds to a pair of PoS tags aligned in the training data.


Indeed, the inability to faithfully reproduce reference translations can have many causes, such as scantiness of the translation table, insufficient expressiveness of reordering models, inadequate scoring function, non-literal references, over-pruned lattices, etc.

quantities such as: where the total weight of a successful path π = ξ 1 .


Aver- age word translation probabilities, w(e i |f j ), are calculated via phrase-pair-internal word alignments.

their method has no notion of translation similarity aside from a bilingual dictionary.


Each phrase pair has a list of associated feature functions (FFs).

we extend the long line of work on inducing translation lexicons (beginning with Rapp (1995)) and propose to use multiple independent cues present in monolingual texts to estimate lexical and phrasal translation probabilities for large, MT-scale phrase-tables.


The automatic measures indicate decent translation performances at this stage which encourages their use in pivot translation that we will discuss in the next section.

transducer-based alignments produce consistently worse translation models (at least in terms of BLEU scores).


In most cases, these unknown words represent information-rich units that bear large portions of the meaning to be translated.

there  Table 6: Training data available for the domain adaptation task.


Nowadays, there are large amounts of data available to train statistical machine translation systems.

bSS is not aimed at optimising computational requirements, but does so as a byproduct.


Hence, we understand that the improvements obtained are provided mainly by a more specialised estimation of the model parameters.

this work was also supported by the Spanish MEC/MICINN under the MIPRCV "Consolider Ingenio 2010" program (CSD2007-00018), and itrans2 (tIN2009-14511) project.


We conducted a small annotation project on Arabic Wikipedia articles.

we consider Arabic Wikipedia articles with diverse topics beyond the commonly-used news domain.


Similarly we expect it to link the event "death" to the time expression "October".

while previous research has mainly focused on temporal links for events only, we deal with both events and fluents with the same method.


This is a bit different from our settings as it required the implementation of a multi-class classifier.

since relations consist of two or more arguments that occur in different, often separated syntactic constituents, a syntactic approach seems futile, especially given our experience with events.


A relation in ACE is defined as an ordered pair of entities appearing in the same sentence which expresses one of the predefined relations.

from this corpus, we removed the files that have not been completed for all three passes.


They also annotated some spurious relation mentions (as adjudicated in adj), although the fraction is smaller (close to 10% of all relation mentions in adj).

subsequence kernel (Bunescu and Mooney, 2005b) or convolution tree kernel (Collins and Duffy, 2001), to calculate their similarity.


We ran LDA and SeededLDA models on the NIPS papers from 2001 to 2010.

this suggests that the quality of the seed topics is determined by the discriminative power of the seed words rather than the number of seed words in each seed topic.


It is important to note as well that, while TOP-ICSUM incorporates an additional layer to model topic distributions at the sentence level, we noted early in our experiments that this did not improve the performance (as evaluated with ROUGE) and consequently relaxed that assumption in Dual-Sum.

for instance, HIERSUM models the intuitions that first sentences in documents should contain more general information, and that adjacent sentences are likely to share specic content vocabulary.


The good news is that this background distribution will contain closed-class words in the language, which are domain-independent (see supplementary material for examples).

the input for DUALSUM is a set of pairs of collections of documents where A i is a base document collection and B i is an update document collection.


On the '04 dataset both models achieve the same performance while on '03 the pairwise model performs significantly (p ≤ 0.05) better than the coverage model.

our coverage features are motivated from the approach of Yue and Joachims (2008).


The difference in BLEU score can be as high as 3 points.

we thus use 'S → T ' when the FO corpus is used for the F2E task or when the EO corpus is used for the E2F task; and 'T → S' when the FO corpus is used for the E2F task or when the EO corpus is used for the F2E task.


Source Cependant, je pense qu'il est prématuré de le faire actuellement,étant donné que le ministre a lancé cette tournée.

(2002) and Moore and Lewis (2010) apply this method to language modeling, while Foster et al.


Furthermore, role In this section, we explain how we generate role-identifying patterns automatically using seed nouns, and we discuss why we add semantic constraints to the patterns when producing labeled instances for training.

finally, we give examples to illustrate how we create training instances.


Figure 5 shows that TIER's performance increases from an F score of 34 when trained on just 100 documents up to an F score of 56 when training on 1,300 documents.

autoSlog-TS (Riloff, 1996) generates lexicosyntactic patterns exhaustively from unannotated texts and ranks them based on their frequency and probability of occurring in relevant documents.


Thus, the event narrative labeller only relies on the output of the two sentence classifiers.

the bars show the range of results across the five runs.


Finding seeding instances of high precision and reasonable coverage is important in bootstrapping.

sentences labeled as event descriptions then proceed to the noun phrase classifiers, which are responsible for identifying the role fillers in each sentence.


In the previous section we described several learning methods that BEAR uses to discover, validate and adapt new event extraction rules.

• A False Alarm occurs when a wrong type of event is extracted (including when no event is present in text).


Figure 11 explains BEAR's learning effectiveness at what we determined empirically to be the optimal confidence threshold (0.5) for pattern acquisition.

our approach uses co-occurrence of both trigger and event argument for detecting new event mentions.


We are not aware of how B&L's permutations were generated, but we assume they are generated in a perfectly random fashion.

but because now π is not guaranteed to be a permutation of σ (there may be repetition or missing values, i.e., −1, in π), Kendall's τ cannot be used, and we use only average continuity and edit distance as dissimilarity metrics in this experiment.


Our permutation generation algorithm is shown in Algorithm 1, where α = 0.05, β = 5.0, MAX NUM = 50, and K and K are two normalization factors to make p(swap num) and p(i, j) proper probability distributions.

as argued by Elsner and Charniak (2011), to better simulate the real situations that human readers might encounter in machine-generated documents, such oracular information should not be taken into account.


Mean average precision reduces to mean reciprocal rank (MRR) when there's only a single answer as in the case of narrative cloze, and would have scored the ranked lists as: Note that mean reciprocal rank has the same issues with guess list length that average rank does.

for the fairy Tale corpus, 1-skip bigrams and 2skip bigrams perform similarly, and both have lower average rank and higher Recall@50 than regular bigrams.


We collect pairs of events that occur with 0, 1 or 2 intervening events, similar to what was done in the 1-skip bigrams strategy.

the probabilities must be defined as: 2 ) = C(e 1 , e 2 ) + C(e 2 , e 1 ) e i e j C(e i , e j ) P (e) = C(e) e C(e ) where C(e 1 , e 2 ) is the number of times that the ordered event pair (e 1 , e 2 ) was counted in the training data, and C(e) is the number of times that the event e was counted.


Fluency edits that exchange co-referents and proper nouns, as illustrated by the example in Table 10, may reoccur frequently but this fact could not be revealed by exact matching of specific proper nouns.

→ "established in") and a deleted segment (")").


Character-level features include counts of deleted, inserted and equal characters of different types, such as word & non-word characters or digits & non-digits.

we refrain from deeper syntactic parsing, Wikipedia-specific features, and language resources that are limited to English.


The resulting score is between 0 and 1, 1 being the first item on the list and 0 being the last.

these three forums are quite representative of online forums on the internet.


This could potentially cause problem during variable estimation and inference.

we could model a user's participation in a thread as a mixture of several different user groups.


@300" shows the average number of threads the top 300 users have participated in.

people in the gaming forum are more specific to the topics they are interested in.


(2010) reported the state-ofthe-art method we used as our EPP baseline.

as a source of evaluation data, we used the British National Corpus (BNC).


EPP computes selectional preferences using distributional similarity, based on the assumption that relatives are likely to appear in the same contexts as relatives seen in the training corpus.

it cannot (and does not) compare them against a fixed threshold to make decisions about selectional preferences.


The first occurrence of the target word in Fig.

to GermaNet, the relations are (mostly) not disambiguated.


With similar arguments it is easy to see that one also has P b = BA b F , p 1 = B α 1 , and p ∞ = α ∞ F .


Furthermore, for each b ∈ X let P b ∈ R l×l denote the matrix whose entries are given by the expected number of occurrences of trigrams.

the advantage of working with operator models is that, under certain mild assumptions on the operator parameters, there exist algorithms that can estimate the operators from observable statistics of the input sequences.


A derivation y of a sentence s 0:N consists of a LEFT and a RIGHT head-modifier sequence for each s t .

second, sVD and matrix operations for computing the operators, which run in time cubic in the number of symbols l. note that when dealing with sparse matrices many of these operations can be performed more efficiently.


In this paper, we propose a new hybrid kernel for RE.

table 4 shows a comparison of the PPI extraction results of our proposed hybrid kernel with those of other state-of-the-art approaches.


• Usually, the e-walk features are constructed using dependency types between {governor of X, node X} and {node X, dependent of X}.

we propose a hybrid kernel by combining the proposed feature based kernel (outlined above) with the Shallow Linguistic (SL) kernel (Giuliano et al., 2006) and the Path-enclosed Tree (PET) kernel (Moschitti, 2004).


PET refers to the path-enclosed tree kernel, SL refers to the shallow linguistic kernel, and TPWF refers to the kernel computed using trigger, pattern, negative cue and walk features.

e.g., pos(stimulatory)−amod− pos(ef f ects) and pos(ef f ects) − amod − pos(stimulatory) are treated as the same feature.


As the algorithm shows, you can use existing algorithms and don't need to have an exact algorithm for the optimization problem, which are features of dual decomposition.

previous studies on coordination disambiguation often dealt only with a restricted form of coordination (e.g.


• A lexicon containing shared words and translation pairs found by applying the languagespecific transformation rules (LEX-2).

first, we start with the intuition that the assumption of symmetry strengthens the confidence of a translation pair.


Suppose we have an Italian word arcipelago, and we would like to detect its correct English translation (archipelago).

that seed lexicon is usually crawled from the Web or obtained from parallel corpora.


After obtaining an exhaustive chart from the coarse stage, the chart is pruned so as to only contain items occurring in the k-best derivations.

in the previous example, the label would become just X A , i.e., the presence of B and C would no longer be required, which enables switching to any binarized production that has covered A as the last node.


With discontinuous treebank parsing the asymptotic complexity of the grammar also plays a major role.

figure 8 compares the parsing times of LCfRS with and without the new CfG-CTf method.


Compared to our new classifier, BEETLE II has lower overall accuracy (0.43 vs. 0.53), but performs micro-and macro-averaged scores.

to show how our predictive models can be used in making implementation decisions, we compare three possible choices for an interpretation component: the original BEEtLE II interpreter, the baseline classifier described earlier, and a new decision tree classifier trained on our data.


Since only a small number of features was included, this limits the applicability of the model we derived from this data set to the systems which make similar types of confusions.

we discuss the results of the intrinsic system evaluation in Section 3.


The general idea of breaking down the student answer into correct, incorrect and missing parts is common in tutorial dialogue systems (Nielsen et al., 2008;Dzikovska et al., 2010b;Jordan et al., 2006).

we applied it to a system that outputs more complex logical forms, but we showed that we could simplify its output to a set of labels which still allowed us to make informed decisions.


2 Ideally, we should add 20 different features to our model, corresponding to every possible confusion.

once a component, such as a parser, is included in a larger system, it is not always clear that improvements in intrinsic evaluation scores will translate into improved overall system performance.


We argue that building such predictive models can help us better evaluate performance of NLP components that cannot be distinguished based on F-score alone, and illustrate our approach by comparing the current interpretation component in the system to a new classifier trained on the evaluation data.

we computed sepa-rate confusion matrices for each student.


The approach shows reasonable performance at individual emotion label prediction, for both emoticons and hashtags.

one of the challenges this poses is that of data annotation: given very large amounts of data, often consisting of very short texts, written in unconventional style and without accompanying metadata, audio/video signals or access to the author for disambiguation, how can we easily produce a gold-standard labelling for training and/or for evaluation and test?


(2) DEPTH, INSTANCE PERCENT, CHILDREN, which is the DEPTH multipled by the INSTANCE PERCENT multiplied by MATCHED CHILDREN.

the actual training and testing examples are generated for pairs of candidate concepts (e.g., <'Film Actors', 'Models'>, <'Film Actors', 'Actors'>, <'Models, 'Actors'>).


(2004,2005) describe a proof-of-concept imple-mentation of a "continuous understanding" module that uses reference information in guiding a bottom-up chart-parser, which is evaluated on a single dialogue transcript.

this score is shown both as absolute  number as well as averaged for each utterance.


For that purpose, knowledge-based approaches employ the structural knowledge encoded in lexical-semantic networks like WordNet (Fellbaum, 1998), while statistical approaches rely on co-occurrence counts collected from large corpora, e.g.

this has a couple of disadvantages: (i) the replacement might be a synonym of the original word and perfectly valid in the given context, (ii) the generated error might be very unlikely to be made by a human, and (iii) inserting artificial errors often leads to unnatural sentences that are quite easy to correct, e.g.


3 The API outputs plain text converted from Wiki-Markup, but the text still contains a small portion of leftover markup and other artifacts.

first, the algorithm removes all closed-class word candidates as well as candidates which cannot be found in the vocabulary.


2 http://code.google.com/p/dkpro-spelling-asl/ real-word spelling errors at some point, which are then corrected in subsequent revisions of the same article.


Influence of the Relatedness Measure As was pointed out before, Budanitsky and Hirst (2006)   show that the measure by Jiang and Conrath (1997) yields the best results in their experiments on malapropism detection.

real-word spelling correction has also been tackled by supervised approaches (Golding and Schabes, 1996;Jones and Martin, 1997;Carlson et al., 2001).


Both test the lexical cohesion of a word with its context.

their dataset cannot be easily used for our purposes and is only available in French, while our framework allows creating datasets for all major languages with minimal manual effort.


The challenge lies in discriminating real-word spelling errors from all sorts of other changes, including non-word spelling errors, reformulations, or the correction of wrong facts.

to the findings of Budanitsky and Hirst (2006), JiangConrath is not the best path-based measure, as Lin provides equal or better performance.


Because there are (to our knowledge) no tools that predict this net effect, it is a matter of empirical investigation (or, in less suave terms, trial-and-error), to determine which corpora to use.

12 the difference is smaller for the experiments with an adapted language model than for those with an out-of-domain one, which confirms that the benefit of language model adaptation and translation model adaptation are not fully cumulative.


For each lexicon pair, the number of overlapping SCFs and the numbers of complementary SCFs are given.

uniform Data Categories for English and German: the main challenge in developing Subcat-LMF has been the specification of DCs (attributes and attribute values) in such a way, that a uniform specification of SCFs in the two languages English and German can be achieved.


Last, we analyze indications of deliberately inserted spelling errors (cf.

these query expansion sets are not suitable for portfolio search.


First, this work is one of the first to evaluate distant supervision for topic identification.

this unintuitive result suggests a new baseline that all topic-based systems should compute.


The present paper can be seen as a generalization of that experiment to more languages and with the notion of code Even though smart paradigm-like descriptions are common in language text books, there is to our knowledge no computational equivalent to the smart paradigms of GF.

of particular interest are (Chan, 2006) and (Dreyer and Eisner, 2011), dealing with the automatic extraction of paradigms from text and investigate how good these can become.


Measurements of the GF Resource Grammar Library show that code size measured in this way is in average 20% of the size of source files in bytes.

the predictability of a lexicon could be defined as a quantity inversely dependent on its cost.


In this way, word-paradigm pairs can be easily converted to morphogical lexica and to transducers that map form descriptions to surface forms and back.

the examples of paradigms that we will show (only informally), can be converted to matching and replacements with regular expressions.


As the prefix pre-appears at the beginning of words, it is identified as a stem.

results are given in Table 6 (Virpioja et al., 2011).


The method proposed in Chan (2006) also learns within a hierarchical structure where Latent Dirichlet Allocation (LDA) is used to find stem-suffix matrices.

, w n = s n + m n }, initial tree T , initial temperature of the system γ, the target temperature of the system κ, temperature decrement η 2: initialise: Remove the leaf node D i that has the word w Draw a split point for the word such that Draw a sibling node D j 7: if p next (D|T ) >= p cur (D|T ) then 10: Accept the new tree structure 11:  γ ← γ − η 24: end while 25: output: A tree structure where each node corresponds to a paradigm.


Most hierarchical clustering algorithms are single-pass, where once the hierarchical structure is built, the structure does not change further.

identifying pre-as a stem does not yield a change in the morphological analysis of the word.


The proportion of ambiguity errors is almost identical for gender, number and rationality.

in this paper, we only discuss systems trained for a single classifier (for gender, for number and for rationality).


In the future, we plan to integrate this task inside of MADA.

unlike him, we do not distinguish between sub-functional (logical and formal) features.


(2007) explicitly showed that Arg2 to Arg5 are semantically inconsistent.

we can acquire information from this sentence that John is an agent of the throwing event (the "Affection" row), as well as a source of the movement event of the ball (the "Movement" row).


Danescu-Niculescu-Mizil and Lee (2010) (henceforth DL10) extend this approach to Romanian, where a comprehensive list of NPIs is not available, by proposing a bootstrapping approach to co-learn DEOs and NPIs.

the first baseline method proposed by DLD09 simply calculates a ratio of the relative frequencies of a word in NPI contexts versus in a general corpus, and the second is a distillation method which appears to refine the baseline ratios using a task-specific heuristic.


However, the problem with this approach is that DEOs are not the only words that co-occur with NPIs.

in fact, as more iterations of EM are run, the performance drops drastically, even though the corpus likelihood is increasing.


We now describe our heuristic method that is based on this intuition.

there exist many piggybackers, which, as defined by DLD09, collocate with DEOs due to semantic relatedness or chance, and would thus incorrectly receive a high S(y) score.


Our method is based on classification of all expressions in subject position, including the recognition of Spanish non-referential impersonal constructions which, to the best of our knowledge, has not yet been addressed.

(e) Se admiten los alumnos que reúnan los requisitos.


We apply the same decoding framework in this paper, but apply an improved training process, and incorporate an N -gram language model into the syntax model.

using online large-margin training not only improves the output quality, but also allows the incorporation of an N -gram language-model into the system.


Humans judge Midge's output to be the most natural descriptions of images generated thus far.

this work learns and builds from these approaches.


After all, if the user does not follow the first instruction, the computation of all remaining instructions was a waste of time.

the second instruction cannot be delayed because this might make the user miss a turn or interpret the instruction incorrectly.


As the order of referents or constituents is arguably influenced by the information structure of a sentence given the previous text, our main assumption was that infor- (1) a. Kurze Zeit später erklärte ein Anrufer bei Nachrichtenagenturen in Pakistan , die Gruppe Gamaa bekenne sich.

many of the relevant discourse factors are reflected indirectly in properties of the sentence-internal material.


On average, each contributor wrote 168 words in 4 turns.

finally, the turn is marked with the indentation level of its least indented paragraph.


The logarithms of the normalized 10-bin histograms for the 7 features were concatenated to result in a single 70-dimensional observation vector for the turn.

the joint probability of Where A is observered and dependent on two hidden variables B and C. Directed edges across time (t − 1 → t) indicate temporal relationships between variables.


Knowledge of fine-grained or subcategorized IS is valuable for other NLP tasks.

while the gold chains alone can distinguish old/general and old/ident generic NPs, the Stanford chains cannot distinguish any of the old subtypes in the absence of other knowledge sources, since it generates chains for all old NPs regardless of their subtypes.


(1) I was angry that he destroyed my tent.

they cannot be used to distinguish between old/general entities and old/generic entities, since neither of them belongs to any chains.


In this case, we drop the lookahead component c from the description.

suppose that we simply want to compose the rules of Figure 2, The bottom rule does not satisfy the requirement that there is at most one input symbol in the left-hand side.


Moreover, it is known (Graehl et al., 2009) that each XTOP F can be transformed into an equivalent XTOP preserving both linearity and nondeletion.

this newly obtained XtOP N is compatible with M .


Here we consider the extended top-down tree transducer (XTOP), which was studied in (Arnold and Dauchet, 1982;Knight, 2007;Graehl et al., 2008;Graehl et al., 2009) and implemented in the toolkit TIBURON (May and Knight, 2006;May, 2010).

each such rule has a distinct state at the root of the left-hand side, which can be used to trigger only this rule.


It can easily be verified that the definition of τ M is independent of the choice of Σ and ∆ .

when meeting a subtree q(p(x)) with q ∈ Q , p ∈ P , and x ∈ X, then we also view this equivalently as the tree q, p (x), which could be part of a rule of our composed XTOP.


For example, starting from a separate SVM for each task, Evgeniou and Pontil (2004) present a regularization method that trades off optimization of the task-specific parameter vectors and the distance of each SVM to the average SVM.

the most similar section or sections -apart from the section itself on the diagonal -is indicated in bold face.


Differences between the text sections become visible in an analysis of token to type ratios.

to that, all models score considerably lower on titles.


Instead of an accidental by-product of phonological and morphological changes, then, a new picture emerges for explaining syncretism in Modern High German definite articles: German speakers have been able to reduce their case paradigm without loss in processing and interpretation efficiency.

the impact of representation on processing is illustrated at the bottom of Figure 1, which shows the performance of a grammar that uses the same processing technique for handling the same utterance, but a different representation than the disjunctive grammar.


The annotators were given the entries as well as the reference sample annotated by the lexicographer and a test sample of 50 concordances for annotation.

the final multiple 50-concordance sample went through one more additional procedure, the adjudication: first, the lexicographer compared the three annotations and eliminated evident errors.


At line 8 the decoder it-erates the main loop, until τ is complete and is returned at line 9.

the score of the interaction between r and the context structure attached to X r,τ can be computed exactly, while the score of the structures attached to other r nonterminals (i.e.


This bag-of-words encoding of minimal units dramatically reduces the vocabulary size but it inevitably maps different MTUs to the same encoding.

informally, we can think of MTUs as small phrase pairs that cannot be broken down any further without violating the two requirements.


This is because frequent words, such as function words, are part of many MTUs and therefore have a very high out-degree, e.g., the neuron representing "the" has over 82K outgoing edges.

we obtain classes for words or MTUs using a version of Brown-Clustering with an additional regularization term to optimize the runtime of the language model (Brown et al., 1992;Zweig and Makarychev, 2013).


Both the baseline and our system are standard phrase-based MT systems.

a segment can also be viewed as a chain, i.e., a graph in which vertices are the segment's words and an edge between two words exists if and only if these words are consecutive.


Since this definition is fairly general it is thus suitable for most NLP tasks.

without going into further details they provide the purpose of normalization.


The monolingual measure is based on the notion of partition refinements and the bilingual measure is based on structural properties of the graph that represents phrase segments and word alignments.

it is tailored to the type of segments that are suitable for the purposes of SMT and is in line with previous work (Blackwood et al., 2008;Paul et al., 2010).


to understand the type of errors that are prevented with the JOINt system, consider the following example (from document WSJ-2428): • [Robert Dow, a partner and portfolio manager at Lord, Abbett & Co.] M 1 , which manages $4 billion of high-yield bonds, says [he] M 2 doesn't "think there is any fundamental economic rationale (for the junk bond rout).


We achieve an absolute average improvement of 7% over a state-of-the-art model.

we follow R10 in evaluating induced event types and orderings in a binary classification setting.


We induce these variables conditioned on all the observable words in the data set w. Since direct joint sampling from the posterior distributions is intractable, we use Gibbs sampling for approximate inference.

to improve mixing time, we also resample components η k i and η l i when word i has changed event membership from type k to type l. In addition we define classes of closely related words (heuristically based on the covariance matrix) by classifying words as related when their similarity exceeds an empirically determined threshold.


We thus conclude that the quality of the data produced by our sense labelling method is sufficient and these data can be directly used for training a statistical VSD classifier.

note that a MFS baseline based on Wn would not be meaningful, because the Wordnet MFS is based on the frequency distribution of annotated senses in SemCor.


We also generated training data automatically for the 305 Senseval verbs.

for instance, we found that some function words (e.g., reflexive pronouns) and some POS tags (e.g., those for past participles and comparative adjectives) introduced too much noise in the data and therefore we did not select them for the final vocabulary.


Freebase (Mintz et al., 2009;Surdeanu et al., 2012) and Ba-belNet (Krause et al., 2012;Moro et al., 2013).

we refer to the resulting data as automatically labelled corpus (ALC) and use it as training data for statistical VSD.


The system achieved only 33.86% and 30.16% accuracy for the MASC and the Senseval-3 verbs, respectively, which is far below the results we presented.

the performance of the system decreased significantly achieving the lowest accuracy among all system configurations.


Moreover, if a lemma appears as a variant in several synsets the most frequent polarity is assigned to that lemma.

the initialization of vector v using the seeds allows the Personalized propagation to assign greater importance to those synsets in the graph identified as being positive and negative, which resuls in a PPV with the weigths skewed towards those nodes initialized/personalized as positive and negative.


This description holds for all other maps in this paper.

this is an improvement over the inappropriate conditional distribution p(M |d) that is obtained by smoothing the tweet map without dividing by the overall tweet distribution p(M ).


This could indicate that the accuracy of a POS tagger on unseen words, especially verbs, may be an important bottleneck in semi-supervised learning.

this has some notional similarity to categories, since, like categories, clusters are less fine-grained than words but more finegrained than POS-tags.


The overall entropy for unseen verb distributions also goes down from 2.23 (supervised) to 1.37 (semisupervised), showing that semi-supervised distributions are more peaked, and bringing them closer to the true entropy of the gold distribution (0.93).

using unlabeled data only to estimate parameters that are badly estimated from labeled data (lexical entries in CCG, due to lexical sparsity) results in improvements, in contrast to prior work with semi-supervised EM.


This morphological lexicon/analyser is language-dependent, usually handcrafted and it has to be compatible with the treebank in question.

these feature sets are engineered for English.


The work presented here complements them with additional semantic relations.

in (9), the analysts 'forecasting' and the company 'saying' do not have as their cause 'planned price cuts'.


We fill caches with words relevant to the topic of the current segment which is being translated.

we analyze the struct-topic cache translations on automatically segmented documents as that provided the best performance overall.


Finally, we present the incremental surface realisation algorithm supporting the verbalisation of the possible query extensions.

conversely, those trees with c wo > 0 get a score that is the word order cost proportional to the covered input.


In effect, this scoring mechanism favors trees with low word order cost and large semantic coverage.

does it ensure that the generated queries respect the strict total order of the query tree linearisation ?


All of the metrics take two inputs: M and C, where M is a workload sen-tence from the MTBT (Material To Be Translated) and C is the source language side of a candidate pre-existing translation from the TM bank.

observing the scatterplots for ED and those for MWNGP one can see that both methods have the vast majority of points concentrated in the lower left and upper right quadrants, solving the upper left quadrant problem of PM.


Recently Simard and Fujita (2012) have experimented with using MT (machine translation) evaluation metrics as TM fuzzy match, or similarity, algorithms.

figure 2 shows that ED has another problem, which is a lot of yellow circles in the lower left quadrant.


Although detailed accounts of the matching algorithms used in commercial systems can't be found in the literature, it is widely believed that edit distance algorithms are used.

a TM metric that matches sentences on more than just (weighted) percentage coverage of lexical items can be expected to perform better for TM bank evaluation and retrieval.


Finally, dimensionality reduction (SVD) is proposed as a way of combating sparsity and random indexing (RI) as an essential step of encoding vectors for use with the convolution operator.

we also consider the combination of normalisation and context selection followed by SVD.


Second, the inclusion of the regression prediction directly into the search allows for finer-grained global decisions as the predictions that the model is more confident about are preferred.

we contrast our work against two data-driven preordering approaches.


The above models are conceptually similar to ours.

models of category learning have been extensively studied in cognitive science and primarily tested on perceptual abstractions or artificial stimuli.


Most existing models focus on adult categorization, in which it is assumed that a large number of categories have already been learnt (but see Anderson 1991 andGriffiths et al.

they rarely co-occur (in fact, a cursory search using Google yields only three matches for the query "polar bear * anteater").


For example, if we consider 1word subsets of Ω, then all cells are needed and GYRO attempts all word permutation.

table 1 gives the results obtained by CCG and GYRO under a 3-gram or a 4-gram LM.


mix rep is the winner or ties for the winner for 25 out of 39 words.


With this purpose, we adopted two techniques.


's algorithm (discussed in Sub-section 3.2.1), and only the n-grams with the highest IG values were selected to be taken as features of the vectors which represented the reviews.

one of these suggestions were also useful for this study, as discussed in Section 2.3.


Our system follows much previous work by counting PPs that accompany the verb among its complements, even though they are not obligatory (so-called 'adjuncts'), because PP adjuncts are excellent clues to a verb's semantics (Sun et al., 2008).

the result is an automatic SCF tagger that is resilient to variations in sentence structure and is firmly based on linguistically motivated knowledge.


We will work with two language models, one based on a large collection of texts sampling from many different authors, the 'general language model', and one based on a set of texts written by an individual, the 'idiolect'.

(1999) indeed shows that the accuracy of a context-sensitive word prediction system is related to how much training material is provided.


The percentage of keystrokes that can be saved when two (and sometimes three) predictions were shown will be referred to as SwiftKey Keystrokes Saved (SKKS).

current popular smartphone applications suggest this approach might be too strict.


Though the improvements are slightly smaller than without adapted language models, there is still a gain over the concatenation baseline of 0.68 BLEU on the mixed test set and similar improvements to before over the benchmarks (on TED the improvements are actually even larger).

we experimented with different numbers of topics for phrasal LDA.


These benchmark datasets have supported a diverse and influential line of research into semantic parsing learning algorithms for sophisticated semantic constructions, with continuing advances in accuracy.

the training objective is to maximize the likelihood of the observed logical Clinical trials data has more coordination, especially noun phrase and adjective phrase coordination, than GeoQuery.


For brevity, we omit the details of how we learn the syntax and mappings from semantics to words or phrases for new lexical entries, but these are borrowed from the existing techniques in UBL.

the focus on these datasets leads to a natural question -do other natural datasets have similar syntax and semantics, and if not, can existing algorithms handle the variability in syntax and semantics?


We chose a train-test split and not cross-validation, since the FCE data set is quite large to allow for such a split.

the hard-decision type-driven approach defines a feature vector for a verb based on its type.


The set of active items is kept in a priority queue and at each step we process the item with the lowest weight.

this can be exemplified with the grammar in Figure 1, where there are two productions for category Conj .


A "fear" hypothesis is selected as a translation with high model score but low metric score.

true post-editing data is infeasible to collect during system development and internal testing as standard MT pipelines require tens of thousands of sentences to be translated with low latency.


received retweets) combined with a significant user impact prediction performance (r = .78) enabled us to investigate further how specific aspects of a user's behaviour relate to impact, by examining the parameters of the inferred model.

this is an intermediate model aiming to evaluate whether the incorporation of text improves prediction performance.


Since the Mult model works very poorly when the input vectors contain negative values, as is the case with SVD, for this model we report result distributions across the 6 NMF variations only.

we used the word cooccurrence vector for a verb or an adjective extracted from the corpus and projected onto the reduced feature space (e.g., NMF, 100 dimensions), then computed cosine similarity between this word meaning representation and its corresponding EnetLex matrix initial and minimumerror intercepts, respectively.


This method has been shown to produce more consistent judgments compared to fluency and adequacy judgments on a five-point scale (Callison-Burch et al., 2007).

english-to-Czech was omitted because of a low response rate on MTurk.


We used the standard NER baseline features (e.g., Dhillon et al., 2011;Ratinov and Roth, 2009) which include: • Current Word w i and its lexical features which include whether the word is capitalized and whether all the characters are cap-italized.

building these dictionaries requires a huge amount of human effort and it is often difficult to get good coverage for many named entity types.